An output formation consensus control for leader–follower networked multi-agent systems under communication constraints and switching topologies

In this paper, the formation consensus problem for a class of leader–follower networked multi-agent systems under communication constraints and switching topologies is investigated. A networked predictive control scheme is proposed to achieve stability and output formation consensus with the switching topology, capable of compensating for data loss and time delays in the network. By equating the whole closed-loop networked multi-agent system with the proposed control scheme to the corresponding switched system, the sufficient and necessary condition of output formation consensus and stability for agents is given. Finally, using three-degree-of-freedom air-bearing spacecraft simulators as the control objects, the proposed scheme is demonstrated to be able to actively compensate for the communication constraints through numerical simulations, and it is also verified to have a good control performance by further realizing the formation task of the simulators through practical experiments.     

An admittance based hierarchical control framework for dual-arm cobots

Dual-arm robotic platforms have solid arguments to match the growing need for versatility in the industry. Coupling the control of two manipulators for cooperative purposes enlarges the scope of feasible operations, while adding perception capabilities allows to navigate in dynamic environments. In this respect, we propose a complete online kinematic control framework for dual-arm robots operating in unstructured industrial settings. We base our approach on admittance control in the cooperative task space. Regulating internal and external efforts offer safe bimanual task execution and enables physical interaction. We implement a hierarchical quadratic programming architecture that applies a prioritization of tasks: most efforts are concentrated on the proper tracking of relative motions of the arms, which is the most critical for safety reasons. We demonstrate the performance of our framework through a “teaching-by-demonstration” experiment on the dual-arm mobile cobot BAZAR.     

Parallel Cloth Simulation Using OpenGL Shading Language

The primary goal of cloth simulation is to express object behavior in a realistic manner and achieve real-time performance by following the fundamental concept of physic. In general, the mass–spring system is applied to real-time cloth simulation with three types of springs. However, hard spring cloth simulation using the mass–spring system requires a small integration time-step in order to use a large stiffness coefficient. Furthermore, to obtain stable behavior, constraint enforcement is used instead of maintenance of the force of each spring. Constraint force computation involves a large sparse linear solving operation. Due to the large computation, we implement a cloth simulation using adaptive constraint activation and deactivation techniques that involve the mass–spring system and constraint enforcement method to prevent excessive elongation of cloth. At the same time, when the length of the spring is stretched or compressed over a defined threshold, adaptive constraint activation and deactivation method deactivates the spring and generate the implicit constraint. Traditional method that uses a serial process of the Central Processing Unit (CPU) to solve the system in every frame cannot handle the complex structure of clothmodel in real-time. Our simulation utilizes the Graphic Processing Unit (GPU) parallel processing with compute shader in OpenGL Shading Language (GLSL) to solve the system effectively. In this paper, we design and implement parallel method for cloth simulation, and experiment on the performance and behavior comparison of the mass–spring system, constraint enforcement, and adaptive constraint activation and deactivation techniques the using GPU-based parallel method.     

Consolidation of Cr3C2 doping WC–1TiN–5MgO micro–nano composites by two-step sintering

The effect of Cr₃C₂ additions on WC–1TiN–5MgO composites by two-step hot-pressing sintering (heated to 1750°C and then immediately cooled to 1575°C with a soaking time of 60?min under a sintering pressure of 50?MPa) was comprehensively investigated. The microstructure was characterized by means of scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), and X-ray diffractometry (XRD). Mechanical properties, such as hardness, transverse rupture strength, and fracture toughness, were measured. The experimental results show that no η-phase or brittle phases such as W₂C were formed, and excellent mechanical properties were achieved for 0.6?wt.% Cr₃C₂ additions with a hardness of 24.76?GPa, a flexural strength of 1257.1?MPa, and a fracture toughness of 10.08?MPa?·?mm^1/2. Cr₃C₂ addition brought about an improvement in the sinterability and contributed to the homogeneous distribution of second phase nanosized MgO. Crack deflection and crack bridging are the major mechanisms contributing to the drastically enhanced flexural strength and fracture toughness.     

A new method using feature extraction for identifying paddy rice species for quality seed selection

The purpose of this work was to explore a new feature extraction method for classifying paddy seeds using a feature extraction algorithm to achieve the area ratio, horizontal–slant and front–rear angles and find whether the proposed features have high discriminating power. Another objective was to find the smallest feature set that can ensure highly accurate recognition of seeds. A total of a 100 image features were extracted, and features having significant discriminating power were identified based on the analysis of variance (ANOVA). From the 100 features, 14 features were found to have high discriminating power and from these features, six were selected as the proposed features. Experimental results show that the proposed features and removal of redundant features enhanced the discriminating power of the feature set, and that the proposed features have an excellent discriminating property for seeds. The presented features resulted in the highest classification accuracy (98.8%) when compared to other methods.     

Semi-analytical solution for free transverse vibrations of Euler–Bernoulli nanobeams with manifold concentrated masses

In the present study, transverse vibrations of nanobeams with manifold concentrated masses, resting on Winkler elastic foundations, are investigated. The model is based on the theory of nonlocal elasticity in the presence of concentrated masses applied to Euler–Bernoulli beams. A closed-form expression for the transverse vibration modes of Euler–Bernoulli beams is presented. The proposed expressions are provided explicitly as the function of two integrated constants, which are determined by the standard boundary conditions. The utilization of the boundary conditions leads to definite terms of natural frequency equations. The natural frequencies and vibration modes of the concerned nanobeams with different numbers of concentrated masses in different positions under some typical boundary conditions (simply supported, cantilevered, and clamped–clamped) have been analyzed by means of the proposed closed–form expressions in order to show their efficiency. It is worth mentioning that the effect of various nonlocal length parameters and Winkler modulus on natural frequencies and vibration modes are also discussed. Finally, the results are compared with those corresponding to a classical local model.     

Fatigue crack growth prediction in nuclear piping using Markov chain Monte Carlo simulation

In this paper, we present and demonstrate a methodology to improve probabilistic fatigue crack growth (FCG) predictions by using the concept of Bayesian updating using Markov chain Monte Carlo simulations. The methodology is demonstrated on a cracked pipe undergoing fatigue loading. Initial estimates of the FCG rate are made using the Paris law. The prior probability distributions of the Paris law parameters are taken from the tests on specimen made of the same material as that of pipe. Measured data on crack depth over number of loading cycles are used to update the prior distribution using the Markov chain Monte Carlo. The confidence interval on the predicted FCG rate is also estimated. In actual piping placed in a plant, the measured data can be considered equivalent to the data received from in-service inspection. It is shown that the proposed methodology improves the fatigue life prediction. The number of observations used for updating is found to leave a significant effect on the accuracy of the updated prediction.     

Compact,ultra-low vibration,closed-cycle helium recycler for uninterrupted operation of MEG with SQUID magnetometers

A closed-cycle helium recycler was developed for continuous uninterrupted operation for magnetometer-based whole-head magnetoencephalography (MEG) systems. The recycler consists of a two stage 4 K pulse-tube cryocooler and is mounted on the roof of a magnetically shielded room (MSR). A flexible liquid helium (LHe) return line on the recycler is inserted into the fill port of the MEG system in the MSR through a slotted opening in the ceiling. The helium vapor is captured through a line that returns the gas to the top of the recycler assembly. A high-purity helium gas cylinder connected to the recycler assembly supplies the gas, which, after it is liquefied, increases the level of LHe in the MEG system during the start-up phase. No storage tank for evaporated helium gas nor a helium gas purifier is used. The recycler is capable of liquefying helium with a rate of ∼17 L/d after precooling the MEG system. It has provided a fully maintenance-free operation under computer control for 7 months without refill of helium. Although the recycler is used for single-orientation operation at this initial testing site, it is designed to operate at ±20° orientations, allowing the MEG system to be tilted for supine and reclining positions. Vibration of the recycler is dampened to an ultra-low level by using several vibration isolation methods, which enables uninterrupted operation during MEG measurements. Recyclers similar to this system may be quite useful even for MEG systems with 100% magnetometers.     

A modified teaching–learning-based optimisation algorithm for bi-objective re-entrant hybrid flowshop scheduling

In this paper, a modified teaching–learning-based optimisation (mTLBO) algorithm is proposed to solve the re-entrant hybrid flowshop scheduling problem (RHFSP) with the makespan and the total tardiness criteria. Based on the simple job-based representation, a novel decoding method named equivalent due date-based permutation schedule is proposed to transfer an individual to a feasible schedule. At each generation, a number of superior individuals are selected as the teachers by the Pareto-based ranking phase. To enhance the exploitation ability in the promising area, the insertion-based local search is embedded in the search framework as the training phase for the TLBO. Due to the characteristics of the permutation-based discrete optimisation, the linear order crossover operator and the swap operator are adopted to imitate the interactions among the individuals in both the teaching phase and the learning phase. To store the non-dominated solutions explored during the search process, an external archive is used and updated when necessary. The influence of the parameter setting on the mTLBO in solving the RHFSP is investigated, and numerical tests with some benchmarking instances are carried out. The comparative results show that the proposed mTLBO outperforms the existing algorithms significantly.     

Drug self-assembly: A phenomenon at the nanometer scale with major impact in the structure–biological properties relationship and the treatment of disease

Under water-rich conditions, small amphiphilic and hydrophobic drug molecules self-assemble into supramolecular nanostructures. Thus, substantial modifications in their interaction with cellular structures and the ability to reach intracellular targets could happen. Additionally, drug aggregates could be more toxic than the non-aggregated counterparts, or vice versa. Moreover, since self-aggregation reduces the number of effective “monomeric” molecules that interact with the target, the drug potency could be underestimated. In other cases, the activity could be ascribed to the non-aggregated molecule while it stems from its aggregates. Thus, drug self-assembly could mislead from drug throughput screening assays to advanced preclinical and clinical trials. Finally, aggregates could serve as crystallization nuclei. The impact that this phenomenon has on the biological performance of active compounds, the inconsistent and often controversial nature of the published data and the need for recommendations/guidelines as preamble of more harmonized research protocols to characterize drug self-aggregation were main motivations for this review. First, the key molecular and environmental parameters governing drug self-aggregation, the main drug families for which this phenomenon and the methods used for its characterization are described. Then, promising nanotechnology platforms investigated to prevent/control it towards a more efficient drug development process are briefly discussed.